Knowledge Gap.
Despite rapid improvements, there remain ample opportunities to broaden the reach of early classifiers.
In particular, existing early classifiers require that their input time series have even spaces between observations: they decide whether to stop early whenever any new observation arrives.
Meanwhile, irregular time series (ITS), which have uneven and often-large gaps between observations, are ubiquitous in impactful domains like healthcare [1]}, climate science [2]}, and astronomy [3]}.
In a hospital, for example, clinicians quantify their patient's health over time by taking measurements (laboratory tests, vital signs, microbiology tests, etc.).
Such ITS are often multivariate and irregular between variables: measuring one variable does not mean measuring all.
